NeT & CoT: Translating Relational Schemas to XML Schemas

1. NeT & CoT: Translating Relational Schemas to XML Schemas Dongwon Lee, Murali Mani, Frank Chiu, Wesley. W. Chu

2. Outline • Problem Definition and Motivation • NeT – Translation using the nest operator in Nested Relational Algebra • CoT – Translation using foreign key constraints between tables • Conclusions and Future directions • Important References

3. What are we studying? <professor Pname=“Muntz” Age=“61”> <student Sname=“MM” Since=“1998”/> <student Sname=“YC” Since=“2000”/> </professor>

4. Why do we study this? • Data exchange - XML has established itself as the standard format for data exchange between applications. • Data integration and “better” data model - XML views of relational data are oftentimes easier to use, and also helps in data integration of multiple data sources.

5. NeT: Using the Nest operator course  (Cname, Prof, Text)

6. NeT (contd…) course  (Cname, Prof +, Text) course  (Cname, Prof +, Text +)

7. NeT (contd…) person  (Name, City, State, Zip) person  (Name+, City, State, Zip)

8. NeT: Formal Definition • Consider Table t with column set C. Nesting on column X is defined as: Any two tuples with the same values for (C – X) will be combined to one tuple • Observation: We need to nest only on key columns

9. CoT: Using foreign key constraints professor  (Pname, Age, student*) student  (Sname, Cname) <professor Pname=“Muntz” Age=“61”> <student Sname=“MM” Cname=“DBs”/> <student Sname=“YC” Cname=“DBs”/> <student Sname=“YC” Cname=“QSs”/> </professor>

10. CoT (contd…) professor  (Pname, Age, student*) student  (Sname, Ref_course) course  (Cname, Since, ID_course)

11. CoT (contd…) <professor Pname=“Muntz” Age=“61”> <student Sname=“MM” Ref_course=“DBs”/> <student Sname=“YC” Ref_course=“DBs”/> <student Sname=“YC” Ref_course=“QSs”/> </professor> <course Cname=“DBs” Since=“1979” ID_course=“DBs”/> <course Cname=“QSs” Since=“1962” ID_course=“QSs”/>

12. CoT algorithm student proj prof emp course dept Top nodes

13. Conclusions • “Better” data model for the user to work with. • Constraints are maintained. • Reduced data redundancy from NeT and CoT – backed by experiments on UCI-KDD repository and TPC-H data. • Implemented and available for use.

14. Lessons Learnt • Helped us learn key issues in XML data modeling: • Local Tree Grammars (such as DTD) sufficient for these applications • The resulting XML Schema did not have any recursion

15. Future Work book  (Btitle, Year) article  (Atitle, Year) person  (Name, Age, Book, Article)

16. Future Work (contd…) If we knew book.person  article.person =  book.person  article.person = person book  (Btitle, Year, person*) article  (Atitle, Year, person*) person  (Name, Age)

17. Future Work (contd…) <person Name=“M” Age=“61”> <person Name=“A” Age=“25”/> <person Name=“B” Age=“31”/> </person>

18. References • Taxonomy of XML Schema Languages using Formal Language Theory, M. Murata, D. Lee, M. Mani, Extreme Markup Languages 2001 • Semantic Data Modeling using XML Schemas, M. Mani, D. Lee, R. R. Muntz, ER 2001